The collusive fraudulent behaviors on e-commerce platforms lead to proliferation of fraudulent reviews, which disrupt fair competition among merchants and mislead consumers’ shopping decisions. Detection of spammer groups helps purify the e-commerce environment and enhances consumers’ shopping experience. However, existing graph-based methods for detecting spammer groups first learn user node vector representations from the graph, and then use clustering methods to obtain candidate groups. Such separate two-stage detection methods are difficult to obtain high-quality candidate groups, resulting in suboptimal detection performance. Additionally, current graph construction methods used in spammer group detection do not fully consider the characteristics of spammer groups, which limits the detection performance. Aiming these concerns, we integrate heterogeneous graph attention network (HGAN) with label propagation (LP) for detecting spammer groups. First, we build a heterogeneous weighted directed (HWD) graph by analyzing the dataset and assign an initial label to each node. Then, we integrate a HGAN-module with an LP-module to obtain the HWD graph’s node embeddings and simultaneously generate candidate groups. We enhance the quality of embeddings and groups through the collaborative optimization between the predicted labels obtained from the HGAN-module and the pseudo-labels obtained from the LP-module. Finally, we calculate the suspiciousness values of groups using the reconstruction loss of the autoencoder for spammer group identification. Experiments conducted on real-world review datasets, including Amazon, Yelp, and YelpChi, demonstrate that our method achieves significant improvements in average Precision@k and Recall@k metrics compared with state-of-the-art baseline approaches.

Abstract As an extension of conventional top-K item recommendation solution, bundle recommendation has aroused increasingly attention. However, because of the extreme sparsity of user-bundle (UB) interactions, the existing top-K item recommendation methods suffer from poor performance when applied to bundle recommendation. While some graph-based approaches have been proposed for bundle recommendation, these approaches primarily leverage the bipartite graph to model the UB interactions, resulting in suboptimal performance. In this paper, a dual hypergraph contrastive learning model is proposed for bundle recommendation. First, we model the direct and indirect UB interactions as hypergraphs to represent the higher-order UB relations. Second, we utilize the hypergraph convolution networks to learn the user and bundle embeddings from the hypergraphs, and improve the learned embeddings through a bidirectional contrastive learning strategy. Finally, we adopt a joint loss that combines the InfoBPR loss supporting multiple negative samples and the contrastive losses to optimize model parameters for prediction. Experiments on the real-world datasets indicate that our model performs better than the state-of-the-art baseline methods.

Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network . First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance , we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon , Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively.

Label propagation-based overlapping community detection algorithms have been widely used in complex networks due to their simplicity and efficiency. However, such algorithms need to randomly choose neighbor nodes and do not fully take the network’s topology into consideration, resulting in low stability and accuracy. Aiming at this problem, we propose an overlapping community detection approach based on DeepWalk and the improved label propagation. We first use the DeepWalk model to learn the network’s topology to obtain low-dimensional vector representations that reflect the spatial location of nodes and construct the weight matrix through vector dot product operation. Then, we design a label propagation algorithm with a preference selection strategy, which can obtain stable overlapping communities by exchanging information with fixed neighbors on the basis of preserving the nodes’ own labels. The experimental results on the real network and synthetic datasets show that the proposed approach has better accuracy and stability than the baseline methods.

In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.

A new metaheuristic algorithm, named surrogate-guided algorithm(S-Jaya), is proposed to solve the single objective continuous optimization problems in this paper. A novel mutation strategy for the non-separable single objective continuous optimization problems is introduced to alter the search engine of the Jaya algorithm. The surrogate is embedded to accelerate the convergence of the population and avoid the proposed algorithm falling into the local optimal during the evolutionary process. The suggested S-Jaya algorithm to address the CEC 2017 benchmark problems is effective and validated. On the quality of solution and execution time, the experimental results reveal that the effectiveness of the S-Jaya algorithm is superior compare with the Jaya algorithm and its variants.